37 lines
2.1 KiB
Markdown
Executable File
37 lines
2.1 KiB
Markdown
Executable File
<img src="https://storage.googleapis.com/ultralytics/UltralyticsLogoName1000×676.png" width="200">
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# Introduction
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This directory contains software developed by Ultralytics LLC. For more information on Ultralytics projects please visit:
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http://www.ultralytics.com
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# Description
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The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. Training is done on the COCO dataset by default: https://cocodataset.org/#home. **Credit to Joseph Redmon for YOLO** (https://pjreddie.com/darknet/yolo/) and to **Erik Lindernoren for the pytorch implementation** this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).
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# Requirements
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Python 3.6 or later with the following `pip3 install -U -r requirements.txt` packages:
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- `numpy`
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- `torch`
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- `opencv-python`
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# Training
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Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full image augmentation, or ~15 epochs/day with no augmentation. Loss plots for bounding boxes, objectness and classification should appear similar to results shown here (training currently in-progress to 160 epochs).
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# Inference
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Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
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# Testing
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Run `test.py` to test the latest checkpoint on the 5000 validation images. Joseph Redmon's official YOLOv3 weights produce a mAP of .581 using this PyTorch implementation, compared to .579 in darknet (https://arxiv.org/abs/1804.02767).
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# Contact
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For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at http://www.ultralytics.com/contact
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